Large language models (LLMs) like ChatGPT have dazzled us with their ability to write stories, translate languages, and even generate code. But can they actually *reason*? A new study suggests they might not be as smart as we think, especially when it comes to math. Researchers have developed a clever technique called "ReasonAgain" that reveals a critical weakness in how LLMs tackle mathematical problems. Instead of simply checking if an LLM gets the right answer, ReasonAgain tests whether the LLM understands the underlying reasoning process. It does this by taking a math problem and subtly changing the numbers, creating several slightly different versions of the original problem. If the LLM genuinely understands the logic, it should be able to solve all these variations correctly. However, the results are alarming. Even the most advanced LLMs, like GPT-4, stumble when presented with these altered problems. While they might get the initial problem right, their performance plummets when the numbers change, revealing a reliance on memorization or shortcuts rather than true understanding. This has significant implications for real-world applications. Imagine an LLM used in finance or engineering making decisions based on flawed reasoning – the consequences could be disastrous. The ReasonAgain method highlights the need for more robust evaluation techniques that go beyond simply checking answers. It challenges us to develop LLMs that truly understand the world, not just parrot back what they’ve been trained on. The quest for truly intelligent AI continues, and this research reminds us that there’s still a long way to go.